Code & Development

Rl

verl is an open-source reinforcement learning (RL) training framework designed specifically for post-training large language models (LLMs). It supports agentic RL training with features such as server-based asynchronous rollout, multi-turn conversations, and tool calls within an agent framework. The framework employs a hybrid programming model that combines single-controller and multi-controller paradigms, allowing flexible representation and execution of complex post-training dataflows. verl integrates with popular LLM infrastructures including PyTorch FSDP, Megatron-LM, vLLM, and SGLang, and offers modular APIs for seamless extension and integration with HuggingFace models. verl is optimized for efficient resource utilization through flexible device mapping and parallelism across GPU clusters. It achieves high throughput by integrating state-of-the-art LLM training and inference frameworks and reduces memory redundancy and communication overhead during training-generation transitions using actor model resharding with its 3D-HybridEngine technology. The framework targets developers and researchers working on RL post-training for LLMs who require scalable and efficient training solutions on GPU clusters.

Updated Feb 10, 2026open-source

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Overview

verl is an open-source RL framework for post-training large language models that supports flexible dataflows and integrates with multiple LLM infrastructures.

Pricing

open-source

Post-Training RL for Large Language Models

Researchers and developers can apply reinforcement learning techniques to fine-tune large language models after initial training to improve performance on specific tasks.

Integration with Existing LLM Infrastructure

Teams using frameworks like PyTorch FSDP or Megatron-LM can extend their workflows by incorporating RL training with verl's modular APIs.

Quick Start

Review Documentation

Visit https://verl.readthedocs.io to understand the framework overview and agent framework details.

Integrate with LLM Infrastructure

Use verl's modular APIs to connect with existing LLM frameworks such as PyTorch FSDP or vLLM.

Build RL Dataflows

Construct reinforcement learning dataflows using the hybrid programming model with minimal code.

Configure Device Mapping

Set up flexible GPU device mapping and parallelism across clusters to optimize training performance.

Train with HuggingFace Models

Leverage ready integration with HuggingFace models for reinforcement learning training.

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Strategic Context for Rl

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Assessment

Strengths

Supports building complex RL dataflows with minimal code.
Integrates seamlessly with multiple popular LLM frameworks.
Achieves high throughput by leveraging state-of-the-art LLM tools.
Reduces memory redundancy and communication overhead via 3D-HybridEngine.
Offers flexible GPU placement for scalability across cluster sizes.

Limitations

Limited to post-training reinforcement learning for large language models.
Requires familiarity with specific LLM frameworks for effective integration.
No publicly available GitHub repository or installation details provided.